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%matplotlib inline
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import math
import torch
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from startorch import sequence as seq
from startorch.utils.plot import hist_sequence, plot_sequence
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plt.style.use("bmh")
plt.rcParams["figure.figsize"] = (16, 5)
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generator = seq.RandAsinhUniform(low=-1000.0, high=1000.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)
RandAsinhUniformSequenceGenerator(low=-1000.0, high=1000.0, feature_size=(1,))
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fig = hist_sequence(generator, bins=500)
fig = hist_sequence(generator, bins=500, scale='asinh')
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generator = seq.RandLogUniform(low=0.001, high=1000.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)
RandLogUniformSequenceGenerator(low=0.001, high=1000.0, feature_size=(1,))
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fig = hist_sequence(generator, bins=500)
fig = hist_sequence(generator, bins=500, scale='log10')
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generator = seq.RandTruncCauchy(loc=0.0, scale=1.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)
RandTruncCauchySequenceGenerator(loc=0.0, scale=1.0, min_value=-2.0, max_value=2.0, feature_size=(1,))
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fig = hist_sequence(generator, bins=500)
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generator = seq.RandTruncExponential(rate=1.0, max_value=5.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)
RandTruncExponentialSequenceGenerator(rate=1.0, max_value=5.0, feature_size=(1,))
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fig = hist_sequence(generator, bins=500)
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generator = seq.RandTruncNormal(mean=1.0, std=2.0, min_value=-2.0, max_value=4.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)
RandTruncNormalSequenceGenerator(mean=1.0, std=2.0, min_value=-2.0, max_value=4.0, feature_size=(1,))
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fig = hist_sequence(generator, bins=500)
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generator = seq.RandTruncHalfCauchy(scale=1.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)
RandTruncHalfCauchySequenceGenerator(scale=1.0, max_value=4.0, feature_size=(1,))
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fig = hist_sequence(generator, bins=500)
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generator = seq.RandTruncHalfNormal(std=1.0, max_value=2.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)
RandTruncHalfNormalSequenceGenerator(std=1.0, max_value=2.0, feature_size=(1,))
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fig = hist_sequence(generator, bins=500)
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generator = seq.RandTruncLogNormal(mean=0.0, std=1.0, max_value=2.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)
RandTruncLogNormalSequenceGenerator(mean=0.0, std=1.0, min_value=0.0, max_value=2.0, feature_size=(1,))
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fig = hist_sequence(generator, bins=500)
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generator = seq.RandUniform(low=-5, high=5)
print(generator)
fig = plot_sequence(generator, batch_size=4)
RandUniformSequenceGenerator(low=-5.0, high=5.0, feature_size=(1,))
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fig = hist_sequence(generator, bins=500)
Continuous univariate supported on a semi-inifinte interval¶
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generator = seq.RandExponential(rate=0.1)
print(generator)
fig = plot_sequence(generator, batch_size=4)
RandExponentialSequenceGenerator(rate=0.1, feature_size=(1,))
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fig = hist_sequence(generator, bins=500, range=(0, 5))
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generator = seq.RandHalfCauchy(scale=1.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)
RandHalfCauchySequenceGenerator(scale=1.0, feature_size=(1,))
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fig = hist_sequence(generator, bins=500, range=(0, 8))
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generator = seq.RandHalfNormal(std=1.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)
RandHalfNormalSequenceGenerator(std=1.0, feature_size=(1,))
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fig = hist_sequence(generator, bins=500, range=(0, 3))
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generator = seq.RandLogNormal(mean=0.0, std=1.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)
RandLogNormalSequenceGenerator(mean=0.0, std=1.0, feature_size=(1,))
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fig = hist_sequence(generator, bins=500, range=(0, 10))
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generator = seq.RandCauchy(loc=0.0, scale=1.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)
RandCauchySequenceGenerator(loc=0.0, scale=1.0, feature_size=(1,))
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fig = hist_sequence(generator, bins=500, range=(-10, 10))
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generator = seq.RandNormal(mean=0.0, std=1.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)
RandNormalSequenceGenerator(mean=0.0, std=1.0, feature_size=(1,))
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fig = hist_sequence(generator, bins=500, range=(-4, 4))
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generator = seq.RandWienerProcess()
print(generator)
fig = plot_sequence(generator, batch_size=10)
RandWienerProcessSequenceGenerator(time_step_size=1.0)
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fig = hist_sequence(generator, bins=500, range=(-100.0, 100.0))
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generator = seq.RandPoisson(rate=5.0)
print(generator)
fig = plot_sequence(generator, batch_size=4)
RandPoissonSequenceGenerator(rate=5.0, feature_size=(1,))
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fig = hist_sequence(generator, bins=100, range=(0, 10))
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generator = seq.Multinomial.create_linear_weights(num_categories=50)
print(generator)
fig = plot_sequence(generator, batch_size=4)
MultinomialSequenceGenerator(num_categories=50, feature_size=(1,))
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fig = hist_sequence(generator, bins=50)
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generator = seq.Multinomial.create_exp_weights(num_categories=50)
print(generator)
fig = plot_sequence(generator, batch_size=4)
MultinomialSequenceGenerator(num_categories=50, feature_size=(1,))
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fig = hist_sequence(generator, bins=50)
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generator = seq.Multinomial.create_uniform_weights(num_categories=50)
print(generator)
fig = plot_sequence(generator, batch_size=4)
MultinomialSequenceGenerator(num_categories=50, feature_size=(1,))
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fig = hist_sequence(generator, bins=50)
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generator = seq.UniformCategorical(num_categories=50)
print(generator)
fig = plot_sequence(generator, batch_size=4)
UniformCategoricalSequenceGenerator(num_categories=50, feature_size=())
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fig = hist_sequence(generator, bins=50)
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generator = seq.RandInt(low=5, high=50)
print(generator)
fig = plot_sequence(generator, batch_size=4)
RandIntSequenceGenerator(low=5, high=50, feature_size=())
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fig = hist_sequence(generator, bins=45)
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